인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Background Artificial Intelligence (AI) is rapidly evolving, presenting both beneficial and challenging implications for society. The critical choice lies in how humanity chooses to harness this technology, particularly in the realm of healthcare diagnostics. This field stands out as a promising area where AI can provide significant assistance, with the potential to transform the diagnostic process into one that is fast, reliable, affordable, repeatable, and accurate. By integrating AI into diagnostic workflows, we can foster evidence-based science in a more efficient manner. All facets of pathological diagnostics can benefit from AI collaboration, which could lead to a transformative future for the industry. Main body This review aims to examine the current advancements of AI in diagnostic applications while offering perspectives on future developments. It covers the fundamental workflows of AI models, highlighting the advantages of unsupervised foundation models in various medical contexts. The discussion explores their utility across disciplines such as histopathology, cytopathology, and hematology, emphasizing their potential to enhance diagnostic accuracy. Additionally, the review addresses existing limitations, challenges faced in implementation, and underscores the ongoing vital role of pathologists in integrating AI into clinical practice. Conclusion The widespread accessibility of data and advanced software tools has significantly propelled and expedited progress in AI research. While the Food and Drug Administration has established regulations to safeguard private information, many researchers persist in developing and training AI models that demonstrate high accuracy. Despite these advancements, challenges remain in deploying fully autonomous AI systems for individual diagnostics. Notably, recent developments in foundation models have shown remarkable potential, surpassing traditional supervised models in diagnosing multiple cancer types, indicating a promising trajectory toward more comprehensive and reliable AI-driven diagnostic solutions in the near future.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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